ai-assisted-performance-review

# AI-Assisted Performance Review Skill The uncomfortable review question of the decade: when a report ships twice the output with AI, what did *they* do? Volume stopped measuring effort; polish stopped measuring skill. Punishing AI use is as wrong as crediting the model's work to the human. This skill separates the signals — and gives managers the conversation, not just the theory. ## What This Skill Produces - A **what-measures-whom analysis** of the role's current evaluation criteria - **Rewritten criteria** that measure the human: judgment, verification, outcomes, leverage - **Calibration rules** for teams with uneven AI adoption - **Conversation scripts** for the three hard cases ## Required Inputs Ask for (if not already provided): - **The role and current review criteria** (the rubric, or how it really works) - **How AI shows up in the work** — which tasks, how much of the output it drafts, what the tooling reality is - **The specific situation**, if any: one person's review? team calibration? criteria rewrite? - **The org's AI stance** — encouraged? tolerated? policy exists? (Reviews must not punish sanctioned behaviour) ## Method 1. **Sort every criterion: human, tool, or hybrid.** Walk the current rubric. Volume of drafts, formatting quality, speed to first version → now mostly **tool** signals (evaluating them evaluates prompt luck and subscription tier). Decision quality, stakeholder trust, error catch rate, what they *chose* to build → still **human**. Outp...